Casual influence coefficients: a localised maximum entropy approach to Bayesian inference

We consider the problem of incomplete conditional probability tables in Bayesian nets, noting that marginal probabilities for an effect, given a single cause are usually easy to elicit and can serve as constraints on the full conditional probability table (CPT) for occurrence of an effect given all possible conditions of its causes. A form of maximum entropy principle, local to an effect node is developed and contrasted with existing global methods.
Exact maximum-entropy CPTs are computed and a conjecture about the exact solution for effects with a general number N of causes is examined.